From passion to practice: clinician teachers’ insights on family medicine obstetrical care
Bibliographic record
Abstract
Background: Declining numbers of family physicians (FPs) provide obstetrical care-an essential service. Exploring reasons why current family medicine obstetrics (FM-OB) clinician teachers chose this field and what motivates them to continue may inform retention strategies and inspire future family medicine learners. Our objective was to explore perspectives of academic FPs who practice FM-OB with the goal of increasing recruitment of future FM-OB practitioners and retention of those currently practicing FM-OB. Methods: Academic FP clinician teachers from three urban multidisciplinary Canadian centers who currently practice FM-OB and deliver at one hospital participated in 60-minute, semi-structured interviews. Questions explored participant experiences providing FM-OB care. Interviews were audio-recorded, transcribed and analyzed using a constant comparison method of descriptive thematic analysis. Results: There were 10 participants. The data revealed an overarching theme highlighting three key influences on the decision to start and continue practicing FM-OB: 1) Individual; 2) Interpersonal, and 3) Systemic influences. Early experiences with positive feedback, hands-on skills, and positive role models shaped their decisions to start. The joy derived from this work, mentorship, patient relationships, and a supportive environment fueled their commitment to continue practicing. Conclusion: This study highlights the importance of early learning experiences, effective role models, and supportive systemic factors in encouraging trainees to practice FM-OB and retaining FPs in this field. By also identifying the sources of joy in FM-OB and promoting work-life balance, these findings can help inform programs to retain FM-OB providers and inspire future family medicine learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".